Gender and sex bias in COVID-19 epidemiological data through the lens of causality
Open accessAbstract
The COVID-19 pandemic has spurred a large amount of experimental and observational studies reporting clear correlation between the risk of developing severe COVID-19 (or dying from it) and whether the individual is male or female. This paper is an attempt to explain the supposed male vulnerability to COVID-19 using a causal approach. We proceed by identifying a set of confounding and mediating factors, based on the review of epidemiological literature and analysis of sex-dis-aggregated data. Those factors are then taken into consideration to produce explainable and fair prediction and decision models from observational data. The paper outlines how non-causal models can motivate discriminatory policies such as biased allocation of the limited resources in intensive care units (ICUs). The objective is to anticipate and avoid disparate impact and discrimination, by considering causal knowledge and causal-based techniques to compliment the collection and analysis of observational big-data. The hope is to contribute to more careful use of health related information access systems for developing fair and robust predictive models.
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Causality can help debugging algorithmic bias mitigation or explaining models, e.g., causal mediation analysis can help uncover disparate impact of models by estimating the fairness associated to different explaining variables.
“Causality can help debugging algorithmic bias mitigation or explaining models [91], e.g., causal mediation analysis can help uncover disparate impact of models by estimating the fairness associated to different explaining variables [92].”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [92]
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- Authors
- Díaz-Rodríguez, N., Binkytė, R., Bakkali, W., Bookseller, S., Tubaro, P., Bacevičius, A., Zhioua, S., & Chatila, R.
- Affiliations
- Universidad de Granada (ES)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Amazon (United States) (US)
- École Pour l'Informatique et les Techniques Avancées (FR)
- Centre National de la Recherche Scientifique (FR)
- OSE Immunotherapeutics (France) (FR)
- Sorbonne Université (FR)
- Corresponding author
- Binkytė, Rūta
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 60
- Issue
- 3
- Pages
- 103276
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- PMC
- PMC9834203
- OpenAlex
- W4315652684
- Cited by
- 15
- References
- 50
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 5
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Processing & Management (publishedVersion, other-oa)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
- Funders
- ERC
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Sex and Gender in Healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- COVID-19 pandemic, sex bias, causal inference, confounding factors, mediation analysis, sex-disaggregated data, indirect discrimination, algorithmic discrimination, health data access
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